Integrating Abduction and Induction in Machine Learning

This article discusses the integration of traditional abductive and inductive reasoning methods in the development of machine learning systems. In particular, it reviews our recent work in two areas: 1) The use of traditional abductive methods to propose revisions during theory re nement, where an existing knowledge base is modi ed to make it consistent with a set of empirical data; and 2) The use of inductive learning methods to automatically acquire from examples a diagnostic knowledge base used for abductive reasoning. Experimental results on real-world problems are presented to illustrate the capabilities of both of these approaches to integrating the two forms of reasoning